This repository contains all of my projects, lab work, and notes I learned from General Assembly's Data Science Immersive Program.
12 week program consisting of python fundamentals to machine learning algorithms using big data visualizations. Data minging, data munging, and data cleaning were easily accomplished pandas and numpy packages. In additon, scrapy and xpath using requests for APIs were used to extract data.
Further analysis needed scikit-learn (machine learning algorithms) such as:
- Kth Nearest Neighbors
- Linear and Logistic Regressions
- SVM _ Ensemble Methods
- Boosting, Bagging
- Decision Trees
- Clustering
- NLP (TfidfVectorizer, CountVectorizer)
Visualizations were created using matplotlib and seaborn packages.
Final capstone project posted here. Airbnb's Superhost classification and predictive analysis
Class of General Assebmly's FIRST DSI program
